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arXiv · 1712.05879

Hierarchical Bayesian Bradley-Terry for Applications in Major League Baseball

Gabriel C. Phelan·John T. Whelan
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Abstract

A common problem faced in statistical inference is drawing conclusions from paired comparisons, in which two objects compete and one is declared the victor. A probabilistic approach to such a problem is the Bradley-Terry model, first studied by Zermelo in 1929 and rediscovered by Bradley and Terry in 1952. One obvious area of application for such a model is sporting events, and in particular Major League Baseball. With this in mind, we describe a hierarchical Bayesian version of Bradley-Terry suitable for use in ranking and prediction problems, and compare results from these application domains to standard maximum likelihood approaches. Our Bayesian methods outperform the MLE-based analogues, while being simple to construct, implement, and interpret.

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BibTeXRIS

Gabriel C. Phelan, John T. Whelan. 2017-12-16. Hierarchical Bayesian Bradley-Terry for Applications in Major League Baseball. https://arxiv.org/abs/1712.05879

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